文章基本信息

标题：WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin
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作者：Gaoyun Shen ; Nengcheng Chen ; Wei Wang 等
期刊名称：Earth System Science Data (ESSD)
印刷版ISSN：1866-3508
电子版ISSN：1866-3516
出版年度：2019
卷号：11
期号：4
页码：1711-1744
DOI：10.5194/essd-11-1711-2019
出版社：Copernicus
摘要：Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, the existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and most of the existing data blending algorithms are not good at removing the day-by-day errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and at a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP; daily, 0.05∘) satellite-derived precipitation developed by UC Santa Barbara over the Jinsha River basin from 1994 to 2014. This method is called the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective for liquid precipitation bias adjustments from points to surfaces as evaluated by multiple error statistics and from different perspectives. Compared with CHIRP and CHIRP with station data (CHIRPS), the precipitation adjusted by the WHU-SGCC method has greater accuracy, with overall average improvements of the Pearson correlation coefficient (PCC) by 0.0082–0.2232 and 0.0612–0.3243, respectively, and decreases in the root mean square error (RMSE) by 0.0922–0.65 and 0.2249–2.9525 mm, respectively. In addition, the Nash–Sutcliffe efficiency coefficient (NSE) of the WHU-SGCC provides more substantial improvements than CHIRP and CHIRPS, which reached 0.2836, 0.2944, and 0.1853 in the spring, autumn, and winter. Daily accuracy evaluations indicate that the WHU-SGCC method has the best ability to reduce precipitation bias, with average reductions of 21.68 % and 31.44 % compared to CHIRP and CHIRPS, respectively. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective at detecting major precipitation events within the Jinsha River basin. In spite of the correction, the uncertainties in the seasonal precipitation forecasts in the summer and winter are still large, which might be due to the homogenization attenuating the extreme rain event estimates. However, the WHU-SGCC approach may serve as a promising tool to monitor daily precipitation over the Jinsha River basin, which contains complicated mountainous terrain with sparse rain gauge data, based on the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at the 0.05∘ resolution over the Jinsha River basin during all four seasons from 1990 to 2014, derived from WHU-SGCC, are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.org/10.1594/PANGAEA.905376, Shen et al., 2019).